Sr. Quantitative Finance Analyst

Bank of America•Jersey City, NJ

2d•$125,000 - $210,000•Onsite

About The Position

The Sr. Quantitative Finance Analyst reviews and validates enterprise fraud prevention and detection models for conceptual soundness and quantitative rigor, ensuring they adhere to sound modeling practices, model governance policies and guidelines, testing playbooks, and regulatory requirements. The role develops testing or alternative models using statistical and quantitative analysis and software such as R, SAS, and Python. It prepares structured, comprehensive documentation of validation results, including Initial Assessment Reports, Model Validation Reports, Required Action Item Assessments, and reviews of Ongoing Monitoring Reports. The analyst partners with business teams, model developers, risk partners, and audit partners throughout model lifecycles and validation processes. The position is based at 525 Washington Blvd, Jersey City, New Jersey, and is part of Bank of America’s in-office culture. It is a first-shift role for 40 hours per week. The annualized salary range is $125,000 to $210,000, with offers determined by experience, education, and skill set. The role is eligible for an annual discretionary incentive plan and company benefits, including paid time off.

Requirements

Master’s degree in a related field or equivalent work experience.
Advanced quantitative degree and 7 or more years of hands-on model development or validation experience in fraud detection or related financial crime areas.
Solid grasp of traditional statistical modeling techniques, such as logistic regression, and advanced AI/ML techniques, such as gradient boosting, neural networks, random forests, and transformers.
Expertise in model evaluation techniques such as rank ordering, ROC curves, confusion matrices, KS, cross-validation, feature importance, and SHAP values.
Proficiency in Python, SAS, R, and LaTeX.
Familiarity with regulatory requirements and guidelines related to risk model validation.
Excellent verbal communication and written documentation capabilities.
Excellent analytical thinking, practical problem-solving skills, and ability to work well with people at all levels.

Nice To Haves

Familiarity with bias and fairness testing for AI/ML models.

Responsibilities

Review and validate enterprise fraud prevention and detection models for conceptual soundness and quantitative rigor.
Ensure models follow sound modeling practices, Model Governance Policy and Guidelines, Testing Playbooks, and regulatory requirements.
Develop testing or alternative models using statistical or quantitative analysis and software such as R, SAS, and Python.
Present validation results in structured, comprehensive reports, including Initial Assessment Reports, Model Validation Reports, Required Action Item Assessments, and Ongoing Monitoring Report reviews.
Partner with and manage interactions among business teams, model developers, risk partners, and audit partners across model lifecycles and validation processes.